• DOI:10.1109/TIT.2010.2040894
Corpus ID: 5487727

Model-Based Compressive Sensing

@article{Baraniuk2010ModelBasedCS,
  title={Model-Based Compressive Sensing},
  author={Richard Baraniuk and V. Cevher and Marco F. Duarte and C. Hegde},
  journal={IEEE Transactions on Information Theory},
  year={2010},
  volume={56},
  pages={1982-2001}
}

Richard Baraniuk, V. Cevher, +1 author C. Hegde

Published 2010

Mathematics, Computer Science

IEEE Transactions on Information Theory

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis. Instead of taking periodic samples, CS measures inner products with M < N random vectors and then recovers the signal via a sparsity-seeking optimization or greedy algorithm. Standard CS dictates that robust signal recovery is possible from M = O(K log(N/K)) measurements. It is possible to… CONTINUE READING

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